| Presentation | 2017-09-15 Quantum-Inspired Regression Forest Zeke Xie, Issei Sato, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal components based on Fraction Transition Probabilities. The idea originating from quantum mechanics can encourage ensemble diversity and the accuracy simultaneously. We incorporate Quantum-Inspired Subspace Method into Random Forest and propose Quantum-Inspired Forest. We theoretically prove that the quantum interpretation corresponds to the first order approximation of ensemble regression. We also evaluate the empirical performance of Quantum-Inspired Forest and Random Forest in multiple hyperparameter settings. Quantum-Inspired Forest prove the significant robustness of the default hyperparameters on most data sets. The contribution of this work is two-fold, a novel ensemble regression algorithm inspired by quantum mechanics and the theoretical connection between quantum interpretations and machine learning algorithms. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Supervised LearningEnsemble MethodRegression TreeFeature SelectionQuantum Physics |
| Paper # | PRMU2017-40,IBISML2017-12 |
| Date of Issue | 2017-09-08 (PRMU, IBISML) |
| Conference Information | |
| Committee | PRMU / IBISML / IPSJ-CVIM |
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| Conference Date | 2017/9/15(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Shinichi Sato(NII) / Kenji Fukumizu(ISM) |
| Vice Chair | Hironobu Fujiyoshi(Chubu Univ.) / Yoshihisa Ijiri(Omron) / Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Hironobu Fujiyoshi(AIST) / Yoshihisa Ijiri(NAIST) / Masashi Sugiyama(Kyoto Univ.) / (Univ. of Tokyo) |
| Assistant | Masato Ishii(NEC) / Yusuke Sugano(Osaka Univ.) / Ichiro Takeuchi(Nagoya Inst. of Tech.) / Toshihiro Kamishima(AIST) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Computer Vision and Image Media |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Quantum-Inspired Regression Forest |
| Sub Title (in English) | |
| Keyword(1) | Supervised LearningEnsemble MethodRegression TreeFeature SelectionQuantum Physics |
| 1st Author's Name | Zeke Xie |
| 1st Author's Affiliation | The University of Tokyo(UTokyo) |
| 2nd Author's Name | Issei Sato |
| 2nd Author's Affiliation | The University of Tokyo(UTokyo) |
| Date | 2017-09-15 |
| Paper # | PRMU2017-40,IBISML2017-12 |
| Volume (vol) | vol.117 |
| Number (no) | PRMU-210,IBISML-211 |
| Page | pp.pp.7-17(PRMU), pp.7-17(IBISML), |
| #Pages | 11 |
| Date of Issue | 2017-09-08 (PRMU, IBISML) |